Most existing relation extraction models make predictions for each entity pair locally and individually, while ignoring implicit global clues available in the knowledge base , sometimes leading to conflicts among local predictions from different entity pairs.

Abstract

And, we find that the clues learnt automatically from existing knowledge bases perform comparably to those refined by human.

Experiments

It uses Freebase as the knowledge base and New York Time corpus as the text corpus, including about 60,000 entity tuples in the training set, and about 90,000 entity tuples in the testing set.

Introduction

Identifying predefined kinds of relationship between pairs of entities is crucial for many knowledge base related applications(Suchanek et al., 2013).

Introduction

Many knowledge bases do not have a well-defined typing system, let alone fine-grained typing taxonomies with corresponding type recognizers, which are crucial to explicitly model the typing requirements for arguments of a relation, but rather expensive and time-consuming to collect.

Introduction

We propose to perform joint inference upon multiple local predictions by leveraging implicit clues that are encoded with relation specific requirements and can be learnt from existing knowledge bases .

Related Work

Their approach only captures relation dependencies, while we learn implicit relation backgrounds from knowledge bases , including argument type and cardinality requirements.

The Framework

The clues of detecting these inconsistencies can be learnt from a knowledge base .

The Framework

As discussed earlier, we will exploit from the knowledge base two categories of clues that implicitly capture relations’ backgrounds: their expected argument types and argument cardinalities, based on which we can discover two categories of disagreements among the candidate predictions, summarized as argument type inconsistencies and Violations of arguments’ uniqueness, which have been rarely considered before.

The Framework

Most existing knowledge bases represent their knowledge facts in the form of (<subject, relation, 0bject>) triple, which can be seen as relational facts between entity tuples.

We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distant supervision.

Introduction

We suggest that a large populated knowledge base should play a key role in syntactic and semantic parsing: in training the parser, in resolving syntactic ambiguities when the trained parser is applied to new text, and in its output semantic representation.

Introduction

Using semantic information from the knowledge base at training and test time will

Introduction

A semantic representation tied to a knowledge base allows for powerful inference operations — such as identifying the possible entity referents of a noun phrase — that cannot be performed with shallower representations (e.g., frame semantics (Baker et al., 1998) or a direct conversion of syntax to logic (B08, 2005)).

Parser Design

These logical forms are constructed using category and relation predicates from a broad coverage knowledge base .

Parser Design

3.1 Knowledge Base

Prior Work

However, these approaches to semantics do not ground the text to beliefs in a knowledge base .

We present a simple, data-driven approach to generation from knowledge bases (KB).

Conclusion

Using the KBGen benchmark, we then showed that the resulting induced FB-LTAG compares favorably with competing symbolic and statistical approaches when used to generate from knowledge base data.

Introduction

In this paper we present a grammar based approach for generating from knowledge bases (KB) which is linguistically principled and conceptually simple.

Introduction

To evaluate our approach, we use the benchmark provided by the KBGen challenge (Banik et al., 2012; Banik et al., 2013), a challenge designed to evaluate generation from knowledge bases ; where the input is a KB subset; and where the expected output is a complex sentence conveying the meaning represented by the input.

Related Work

With the development of the semantic web and the proliferation of knowledge bases, generation from knowledge bases has attracted increased interest and so called ontology verbalisers have been proposed which support the generation of text from (parts of) knowledge bases .

Related Work

strand of work maps each axiom in the knowledge base to a clause.

Related Work

The MIAKT project (Bontcheva and Wilks., 2004) and the ONTOGENERATION project (Aguado et al., 1998) use symbolic NLG techniques to produce textual descriptions from some semantic information contained in a knowledge base .

The KBGen Task

Specifically, the task is to verbalise a subset of a knowledge base .

The KBGen Task

The KB subsets forming the KB Gen input data were preselected from the AURA biology knowledge base (Gunning et al., 2010), a knowledge base about biology which was manually encoded by biology teachers and encodes knowledge about events, entities, properties and relations where relations include event-to-entity, event-to-event,

Our task is as follows: Given (i) a knowledge base IC, and (ii) a training set of question-answer pairs 3/1)};1, output a semantic parser that maps new questions at to answers 3/ via latent logical forms 2.

The intuition of the paradigm is that one can take advantage of several knowledge bases , such as WordNet3, Freebase4 and YAGO5, to automatically label free texts, like Wikipedia6 and New York Times corpora7, based on some heuristic alignment assumptions.

Introduction

>) are not only involved in the relation instances8 coming from knowledge bases (President—of(Barack Obama, U.S.) and Born—in (Barack Obama, U .

Related Work

(2004) used WordNet as the knowledge base to discover more h-pyernym/hyponym relations between entities from news articles.

(2012) proposed a novel approach to multi-instance multi-label learning for relation extraction, which jointly modeled all the sentences in texts and all labels in knowledge bases for a given entity pair.

A typical knowledge-based question answering (KB-QA) system faces two challenges: one is to transform natural language questions into their meaning representations (MRs); the other is to retrieve answers from knowledge bases (KBs) using generated MRs.

Compared to their work, our method gains an improvement in two aspects: (1) Instead of using facts extracted using the open IE method, we leverage a large scale, high-quality knowledge base ; (2) We can handle multiple-relation questions, instead of single-relation queries only, based on our translation based KB-QA framework.

Introduction

(2013) is one of the latest work which has reported QA results based on a large scale, general domain knowledge base (Freebase), we consider their evaluation result on WEBQUESTIONS as our baseline.

Wikification for tweets aims to automatically identify each concept mention in a tweet and link it to a concept referent in a knowledge base (e.g., Wikipedia).

Experiments

We use a Wikipedia dump on May 3, 2013 as our knowledge base , which includes 30 million pages.

Introduction

concept referent in a knowledge base (KB) (e.g., Wikipedia).

Principles and Approach Overview

Knowledge Base (Wikipedia)

Related Work

The task of linking concept mentions to a knowledge base has received increased attentions over the past several years, from the linking of concept mentions in a single text (Mihalcea and Csomai, 2007; Milne and Witten, 2008b; Milne and Witten, 2008a; Kulkami et al., 2009; He et al., 2011; Ratinov et al., 2011; Cassidy et al., 2012; Cheng and Roth, 2013), to the linking of a cluster of corefer-

The triple format is the most common representation of facts in knowledge bases .

Fact Candidates

NELL’s entity typing method has high recall because when entities are not in the knowledge base , it performs on-the-fly type inference using the Web.

Frequent bi- grams

We evaluated FactChecker on three datasets: i) KB Fact Candidates: The first dataset consists of fact candidates taken from the fact extraction pipeline of a state-of-the-art knowledge base , NELL (Carlson et al., 2010).

Frequent bi- grams

ii) Wikipedia Fact Candidates: For the second dataset, we did not restrict the fact candidates to specific topics from a knowledge base , instead we aimed to evaluate all fact candidates about a given entity.

Introduction

These projects have produced knowledge bases containing many millions of relational facts between entities.

Given the logical forms for a pair of sentences, a text T and a hypothesis H, and given a set of weighted rules derived from the distributional semantics (as explained in section 2.6) composing the knowledge base KB, we build a PSL model that supports determining the truth value of H in the most probable interpretation (i.e.

PSL for STS

KB: The knowledge base is a set of lexical and phrasal rules generated from distributional semantics, along with a similarity score for each rule (section 2.6).

Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing.

Abstract

Those efforts map questions to sophisticated meaning representations that are then attempted to be matched against Viable answer candidates in the knowledge base .

Introduction

Question answering (QA) from a knowledge base (KB) has a long history within natural language processing, going back to the 1960s and 1970s, with systems such as Baseball (Green Jr et al., 1961) and Lunar (Woods, 1977).